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Genetic Relational Search for Inductive Concept Learning (Paperback)
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Genetic Relational Search for Inductive Concept Learning (Paperback)
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Learning from examples in First Order Logic, also known as
Inductive Logic Programming (ILP), constitutes a central topic in
Machine Learning, with relevant applications to problems in complex
domains, e.g., natural language and computational biology. Learning
can be viewed as a search problem in the space of all possible
hypotheses. Given a background knowledge, a set of positive
examples and a set of negative examples, expressed in First Order
Logic, one has to find a hypothesis which covers all positive
examples and none of the negative ones. This problem is NP-hard
even if the language to represent hypotheses is propositional
logic. When FOL hypotheses are used, this complexity is combined
with the complexity of evaluating hypotheses. This book describes
an evolutionary algorithm for ILP. The algorithm, called ECL (for
Evolutionary Concept Learner), evolves a population of Horn clauses
by repeated selection, mutation and optimization of more fit
clauses. ECL relies on four greedy mutation operators for searching
the hypothesis space, and employs an optimization phase that
follows each mutation. Experimental results show that ECL works
well in practice.
General
Imprint: |
Lap Lambert Academic Publishing
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Country of origin: |
Germany |
Release date: |
September 2010 |
First published: |
September 2010 |
Authors: |
Federico Divina
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Dimensions: |
229 x 152 x 11mm (L x W x T) |
Format: |
Paperback - Trade
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Pages: |
192 |
ISBN-13: |
978-3-8433-5548-3 |
Categories: |
Books >
Computing & IT >
General theory of computing >
General
|
LSN: |
3-8433-5548-7 |
Barcode: |
9783843355483 |
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